Phylogenetic Characterization of the Energy-taxis Receptor Aer in <i>Pseudomonas</i> and Phenotypic Characterization in <i>P. pseudoalcaligenes</i> KF707
Bibliographic record
Abstract
Abstract Chemotaxis allows bacteria to sense gradients in their environment and respond by directing their swimming. Aer is a receptor that, instead of responding to a specific chemoattractant, allows bacteria to sense cellular energy levels and move towards favourable environments. In Pseudomonas , the number of apparent Aer homologs differs between the only two species it had been characterized in, P. aeruginosa and P. putida . Here we combined bioinformatic approaches with deletional mutagenesis in P. pseudoalcaligenes KF707 to further characterize Aer. It was determined that the number of Aer homologs varies between 0-4 throughout the Pseudomonas genus, and they were phylogenetically classified into 5 subgroups. We also used sequence analysis to show that these homologous receptors differ in their HAMP signal transduction domains. Genetic analysis also indicated that some Aer homologs have likely been subject to horizontal transfer. P. pseudoalcaligenes KF707 was unique among species for having three Aer homologs as well as the receptors CttP and McpB. Phenotypic characterization in this species showed the most prevalent homolog of Aer was key, but not essential for energy-taxis. This study demonstrates that energy-taxis in Pseudomonas varies between species and provides a new naming convention and associated phylogenetic details for Aer chemoreceptors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".